In this work we introduce CUTS (Contrastive and Unsupervised Training for Segmentation),
a fully unsupervised deep learning framework for medical image segmentation to better …

Segmenting medical images is critical to facilitating both patient diagnoses and quantitative
research. A major limiting factor is the lack of labeled data, as obtaining expert annotations …

Segmenting medical images is critical to facilitating both patient diagnoses and quantitative
research. A major limiting factor is the lack of labeled data, as obtaining expert annotations …

Recent advancements in medical imaging and artificial intelligence (AI) have greatly
enhanced diagnostic capabilities, but the development of effective deep learning (DL) …

Medical image segmentation remains particularly challenging for complex and low-contrast
anatomical structures. In this paper, we introduce the U-Transformer network, which …

Medical image segmentation has seen significant progress through the use of supervised
deep learning. Hereby, large annotated datasets were employed to reliably segment …

The segmentation of medical images is a fundamental step in automated clinical decision
support systems. Existing medical image segmentation methods based on supervised deep …

Image segmentation is a ubiquitous step in almost any medical image study. Deep learning-
based approaches achieve state-of-the-art in the majority of image segmentation …

Supervised deep learning methods have gained prevalence in various medical image
segmentation tasks for the past few years, such as U-Net and its variants. However, most …

Accurate image segmentation is important for many downstream clinical applications like
diagnosis, surgery planning. In recent years, deep neural networks have been quite …